 Power generation from medium temperature geothermal resources: ANN-based optimization of Kalina cycle system-34Oguz Arslan Energy, 2011, vol. 36, issue 5, 2528-2534 Abstract: Recent technical developments have made it possible to generate electricity from geothermal resources of low and medium enthalpy. One of these technologies is the Kalina Cycle System. In this study, electricity generation from Simav geothermal field was investigated using the Kalina cycle system-34 (KCS-34). However, the design of these technologies requires more proficiency and longer times within complex calculations. An artificial neural network (ANN) is a new tool used to make a decision for the optimum working conditions of the processes within the expertise. In this study, the back-propagation learning algorithm with three different variants, namely Levenberg–Marguardt (LM), Pola–Ribiere Conjugate Gradient (CGP), and Scaled Conjugate Gradient (SCG), were used in the network so that the best approach could be found. The most suitable algorithm found was LM with 7 neurons in a single hidden layer. The obtained weights were used in optimization process by coupling the life-cycle-cost concepts. Keywords: Life cycle cost; Levenberg–Marquardt; Pola–Ribiere conjugate gradient; Scaled conjugate gradient; Multi-layer neural network; Kalina cycle system-34 (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:36:y:2011:i:5:p:2528-2534 DOI: 10.1016/j.energy.2011.01.045 Access Statistics for this article Energy is currently edited by Henrik Lund and Mark J. Kaiser More articles in Energy from Elsevier |
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